Artificial intelligence (AI) holds considerable potential for accelerating sustainability transitions, yet it also introduces social, ethical, and environmental risks. As AI systems become embedded in socio-technical infrastructures, their responsible design and governance are essential to align them with human well-being, environmental sustainability, and societal equity. This paper introduces a conceptual framework, Human-Centered Sustainable AI (HCS-AI), which positions AI design and deployment at the intersection of three interrelated dimensions: Ethical Intelligence, Participatory Intelligence, and Ecological Intelligence (the environmental sustainability of AI systems, comprising energy use, carbon emissions, resource usage, and a lifecycle approach). The framework synthesizes literature on green AI, explainable AI, foundation-model risks, and participatory or citizen-science approaches into design principles, methodological guidance, and indicative metrics. It is a literature-based conceptual contribution and has not been empirically validated. Its potential relevance is discussed for priority application areas, namely participatory digital governance, urban mobility, and environmental monitoring, together with the boundary conditions under which it should be applied only in reduced form or not at all. The paper concludes with technical and governance challenges and a research agenda linking AI research, public policy, and civic engagement.
Nature-based solutions (NBS) are increasingly promoted for flood mitigation, yet how to allocate limited resources while balancing aggregate effectiveness and social equity remains poorly understood. In particular, it is unclear whether directing greater mitigation benefits toward socially vulnerable communities necessarily requires substantial losses in overall flood-susceptibility reduction. We develop an equity-aware multi-objective optimization framework for allocating tree planting and wetland restoration across census block groups under fixed budget, suitability, and intervention-capacity constraints. The framework explicitly represents the trade-off between aggregate flood-susceptibility reduction and vulnerability-weighted mitigation benefit, while high-priority locations are identified using a composite flood risk metric integrating flood susceptibility, population exposure, and social vulnerability. We apply the framework to Dorchester and Somerset Counties, Maryland, and evaluate exact optimization alongside greedy, random, and reinforcement-learning benchmarks. Under a USD 20 million budget, moderate vulnerability weighting produced substantial gains in vulnerability-weighted mitigation benefit with little loss in aggregate performance. In Dorchester, aggregate susceptibility reduction changed from 33.47
Soil contamination by cadmium (Cd) represents a major constraint to crop productivity because of its ability to disrupt plant growth, photosynthesis, cellular redox homeostasis, and metal homeostasis. Cadmium (Cd) induced toxicity was assessed in Brassica napus L. using a factorial design with two levels of Cd (0 and 250 μM CdCl2), two levels of exogenous Melatonin (ML; 0 and 100 μM) and two levels of exogenous Silymarin (SIL; 0 and 200 μM). The Cd exposure caused significant alterations in plant growth, photosynthetic pigment, chlorophyll fluorescence, gas exchange properties, water status, accumulation of osmolyte, oxidative stress markers, antioxidant defence, redox metabolism and accumulation and translocation of Cd. Specifically, Cd had a significant effect on total chlorophyll, Fv/Fm, ΦPSII, NPQ, photosynthetic rate, stomatal conductance, transpiration, relative water content, proline, glycine betaine, H2O2, electrolyte leakage, and some parts of the enzymatic and ascorbate–glutathione defence system. Exogenous application of ML and SIL greatly changed some Cd-sensitive physiological and biochemical parameters and enhanced the plant growth performance in Cd-stressed conditions. The factorial analysis also showed significant Cd × ML and Cd × SIL interactions for some traits, whereas Cd × SIL × ML interaction was significant for some parameters such as total chlorophyll, NPQ, stomatal conductance, relative water content, proline, SOD, APX, DHAR, MDHAR, GSH and shoot Cd concentration and Cd translocation factor. Such interaction effects suggested that the effect of ML and SIL was dependent on Cd exposure and for some traits, the combined application of ML and SIL. The antioxidant, redox, photosynthesis and Cd homeostasis-related responses were also modulated by ML and SIL, in line with the physiological benefits and changes in Cd partitioning under stress conditions. The observed responses are not however seen as conclusive evidence of synergism, since formal synergy analysis was not carried out. The overall results showed that Cd exposure caused various physiological and biochemical changes in B. napus, and that Cd application along with exogenous ML and/or SIL was effective in significantly reducing the effect of Cd on some physiochemical traits with significant interaction effects. Further studies include direct quantification of ML and SIL uptake, distribution into tissues, metabolism, and field-level validation for determination of their mechanistic and agronomic applicability.
Hospitals offer important opportunities to improve the environmental sustainability of institutional foodservices, yet environmental assessments often remain descriptive and provide limited support for menu-planning decisions. This study develops and demonstrates a result-derived decision-support framework designed to translate meal-level life cycle assessment (LCA) into intervention priorities for hospital menu planning. The framework was applied to seven routinely served lunches in a public teaching hospital in Iran. Primary data on ingredients, energy use, packaging, kitchen waste, and plate-waste data from 539 eligible served-meal observations were integrated into foreground life cycle inventories. Environmental performance was assessed using IPCC 2021 GWP100, ReCiPe 2016 Midpoint (H), ReCiPe 2016 Endpoint (H), and AWARE, followed by hotspot identification and four intervention scenarios. Environmental rankings varied across impact categories. Red-meat-based meals generated the highest impacts for climate change, human health, and ecosystem quality, whereas the trout-based meal was the dominant water-scarcity hotspot. Mass-equivalent replacement of the primary animal-protein ingredient with tofu produced the largest environmental reductions across most categories, especially for red-meat-based meals, but was not designed to represent nutritional equivalence. Reducing only downstream organic waste flows produced negligible changes, whereas improved cooking-fuel efficiency generated smaller but consistent reductions. The framework classifies hotspots, matches interventions, verifies cross-category trade-offs, screens local feasibility, and prioritizes actions. It is intended as a reusable decision logic, while external validation in additional hospitals is required to establish empirical transferability. A result-derived decision-support framework for sustainable hospital menu planning was developed. Meal-level LCA integrated measured plate waste from 539 eligible served-meal observations. Multi-method LCIA revealed environmental trade-offs across impact categories. Protein substitution reduced impacts more effectively than downstream waste-flow reduction. The framework enables evidence-based environmental decision-making in institutional foodservices.
The use of mathematical models, such as Data Envelopment Analysis (DEA), has become increasingly frequent in the literature on water resources management. Given the pressures posed by climate change and water scarcity, this review examines how DEA has been applied in studies in this field. Specifically, the review included 99 articles selected through a systematic review process following the PRISMA protocol. These articles were analyzed according to six categories: (i) general information, (ii) application context and sector, (iii) DEA model and orientation, (iv) input–output structure, (v) DEA advanced extensions, (vi) limitations and practical implications reported in the literature. The main findings indicate a predominance of studies at the national level, although the primary scale of application is regional/provincial, with sample size typically ranging from 11 to 30 DMUs. There is a recurring use of variable returns-to-scale models, with an emphasis on economic and productivity inputs and outputs associated with water resources. Extensions focused on performance discrimination and benchmarking stand out, indicating a concern with the analytical and comparative capacity of the applied models. The data are limited in terms of frequency, availability, quality, and granularity. The main practical implications concern public policies related to water governance, including regulatory design and resource allocation. The review reveals a tendency toward analytical reductionism, with limited attention to social dimensions and uncertainty. Drawing on the findings of this review and the corpus analyzed, a ten-step guide is proposed for applying DEA in water resources management.
